The SYSCID consortium aims to develop a systems medicine approach for disease prediction in CID. We will focus on three major CID indications with distinct characteristics, yet a large overlap of their molecular risk map: inflammatory bowel disease, systemic lupus erythematodes and rheumatoid arthritis. We have joined 15 partners from major cohorts and initiatives in Europe (e.g.IHEC, ICGC, TwinsUK and Meta-HIT) to investigate human data sets on three major levels of resolution: whole blood signatures, signatures from purified immune cell types (with a focus on CD14 and CD4/CD8) and selected single cell level analyses. Principle data layers will comprise SNP variome, methylome, transcriptome and gut microbiome. SYSCID employs a dedicated data management infrastructure, strong algorithmic development groups (including an SME for exploitation of innovative software tools for data deconvolution) and will validate results in independent retrospective and prospective clinical cohorts. Using this setup we will focus on three fundamental aims : (i) the identification of shared and unique "core disease signatures” which are associated with the disease state and independent of temporal variation, (ii) the generation of "predictive models of disease outcome"- builds on previous work that pathways/biomarkers for disease outcome are distinct from initial disease risk and may be shared across diseases to guide therapy decisions on an individual patient basis, (iii) "reprogramming disease"- will identify and target temporally stable epigenetic alterations in macrophages and lymphocytes in epigenome editing approaches as biological validation and potential novel therapeutic tool . Thus, SYSCID will foster the development of solid biomarkers and models as stratification in future long-term systems medicine clinical trials but also investigate new causative therapies by editing the epigenome code in specific immune cells, e.g. to alleviate macrophage polarization defects.

Recent progress in various areas of physics has demonstrated our ability to control quantum effects in customized systems and materials, thus paving the way for a promising future for quantum technologies. The emergence of such quantum devices, however, requires one to understand fundamental problems in non-equilibrium statistical physics, which can pave the way towards full control of quantum systems, thus reinforcing new applications and providing innovative perspectives. This project is dedicated to the study and the control of out-of-equilibrium properties of quantum many-body systems which are driven across phase transitions. Among several approaches, it will mainly focus on slow quenches and draw on the understanding delivered by the Kibble-Zurek (KZ) mechanism. This rather simple paradigm connects equilibrium with out-of-equilibrium properties and constitutes a benchmark for scaling hypothesis. It could pave the way towards tackling relevant open questions, which lie at the heart of our understanding of out-of-equilibrium dynamics and are key issues for operating in a robust way any quantum simulator. Starting from this motivation, we will test the limits of validity of the KZ dynamics by analyzing its predictions, thus clarifying its predictive power, and extend this paradigm to quantum critical systems with long-range interactions and to topological phase transitions. We will combine innovative theoretical ideas of condensed-matter physics, quantum optics, statistical physics and quantum information, with advanced experiments with ultracold atomic quantum gases. Quantum gases are a unique platform for providing model systems with the level of flexibility and control necessary for our ambitious goal. Their cleanness and their robustness to decoherence will greatly enhance the efficient interplay between theory and experiments, and provide a platform of studies whose outcomes are expected to have a strong scientific impact over a wide range of disciplines. On the short time scale we will exploit this knowledge to develop viable protocols for quantum simulators. In general, we expect that the results of this project will lay the ground for the development of the next generation of quantum devices and simulators.

Control systems are not only at the core of most engineering systems but also form the backbone of critical infrastructures, such as distribution systems for electricity and water, transportation networks, and mobile communications. The increasing network connectivity of control systems has highlighted the necessity for ensuring resilient and secure operation against cyber attacks, which have become increasingly sophisticated over the years and have often had severe repercussions. At the same time, by nature, control systems combine characteristics that render the provision of cybersecurity challenging. Existing security mechanisms typically ignore the cyber-physical nature of networked control systems and either focus on the cyber or physical domain exclusively. Such narrow viewpoints not only impair any proposed security mechanisms but also result in limited models for underlying systems and attacks. Academic contributions are typically distant from practical industry challenges, simply resulting in an isolated view on the IT security properties of confidentiality, integrity, or availability (CIA), without providing comprehensive security solutions. Adding insult to injury, proposed security mechanisms are rarely validated via suitable testbeds or experiments in the field. This DN intends to bridge the gap between theory and practice for secure and resilient cyber-physical systems by tackling the problem at its root. It brings together domain experts from control theory, computer science, and major industrial stakeholders to boost the security and resilience of control systems by outlining a sustainable program for training the next generation of innovators in this critical domain. It pursues a holistic approach that combines specialized training (in both academia and industry), while identifying challenging characteristics and security flaws in existing legacy systems, and pinpointing design requirements and solutions in future (networked) control systems.